Mohammed Elgarhy¹, Amal S. Hassan², Najwan Alsadat³, Oluwafemi Samson Balogun⁴, Ahmed W. Shawki⁵, Ibrahim E. Ragab^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2547-2583, 2025, DOI:10.32604/cmes.2025.058362 - 03 March 2025

Abstract Accurately modeling heavy-tailed data is critical across applied sciences, particularly in finance, medicine, and actuarial analysis. This work presents the heavy-tailed power XLindley distribution (HTPXLD), a unique heavy-tailed distribution. Adding one more parameter to the power XLindley distribution improves this new distribution, especially when modeling leptokurtic lifetime data. The suggested density provides greater flexibility with asymmetric forms and different degrees of peakedness. Its statistical features, like the quantile function, moments, extropy measures, incomplete moments, stochastic ordering, and stress-strength parameters, are explored. We further investigate its use in actuarial science through the computation of pertinent metrics,… More >

Sanaa Al-Marzouki¹, Farrukh Jamal², Christophe Chesneau^3,*, Mohammed Elgarhy⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 437-458, 2020, DOI:10.32604/cmes.2020.011521 - 18 September 2020

Abstract Recent studies have pointed out the potential of the odd Fréchet family (or class) of continuous distributions in fitting data of all kinds. In this article, we propose an extension of this family through the so-called “Topp-Leone strategy”, aiming to improve its overall flexibility by adding a shape parameter. The main objective is to offer original distributions with modifiable properties, from which adaptive and pliant statistical models can be derived. For the new family, these aspects are illustrated by the means of comprehensive mathematical and numerical results. In particular, we emphasize a special distribution with More >